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EvoDeep: A new evolutionary approach for automatic Deep Neural Networks parametrisation

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EvoDeep: A new evolutionary approach for automatic Deep Neural Networks parametrisation

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Martín, A.; Lara-Cabrera, R.; Fuentes-Hurtado, FJ.; Naranjo Ornedo, V.; Camacho, D. (2018). EvoDeep: A new evolutionary approach for automatic Deep Neural Networks parametrisation. Journal of Parallel and Distributed Computing. 117:180-191. https://doi.org/10.1016/j.jpdc.2017.09.006

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/146154

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Title: EvoDeep: A new evolutionary approach for automatic Deep Neural Networks parametrisation
Author: Martín, Alejandro Lara-Cabrera, Raúl Fuentes-Hurtado, Félix José Naranjo Ornedo, Valeriana Camacho, David
UPV Unit: Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions
Issued date:
Abstract:
[EN] Deep Neural Networks (DNN) have become a powerful, and extremely popular mechanism, which has been widely used to solve problems of varied complexity, due to their ability to make models fitted to non-linear complex ...[+]
Subjects: Deep learning , Evolutionary algorithms , Finite-State machines , Automated parametrisation
Copyrigths: Reserva de todos los derechos
Source:
Journal of Parallel and Distributed Computing. (issn: 0743-7315 )
DOI: 10.1016/j.jpdc.2017.09.006
Publisher:
Elsevier
Publisher version: https://doi.org/10.1016/j.jpdc.2017.09.006
Thanks:
This work has been co-funded by the next research projects: EphemeCH (TIN2014-56494-C4-4-P) and DeepBio (TIN2017-85727-C4-3-P) Spanish Ministry of Economy and Competitivity and European Regional Development Fund FEDER, ...[+]
Type: Artículo

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